EEFFL: energy efficient data forwarding for forest fire detection using localization technique in wireless sensor network

Vikram, Raj; Sinha, Ditipriya; De, Debashis; Das, Ayan

doi:10.1007/s11276-020-02393-1

Cited by 35 publications

(10 citation statements)

References 34 publications

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“…The wireless communication network system formed by sensor nodes through self-organizing network is called Wireless Sensor Network (WSN), which is composed of common sensor nodes, aggregation nodes, and terminal management system 1 , and has been widely used in environmental monitoring, national defense and military fields 2 . Wireless sensor networks are usually deployed in grasslands, deep seas, forests, or locations that are untouchable by humans, making the sensor nodes a one-time deployment that will not be recycled 3 .…”

Section: Introductionmentioning

confidence: 99%

Wireless sensor networks multi-strategy clustering mechanism based on improved sparrow search algorithm

Lin¹,

Ma²,

Luo³

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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To solve the problem of uneven energy consumption of routing protocols for wireless sensor networks (WSN), a WSN multi-strategy clustering mechanism based on improved sparrow search algorithm is proposed, which optimizes the intra-cluster task using a dual cluster head clustering strategy. Firstly, a node hierarchy strategy based on location information is designed; secondly, a multinomial constraint factor is designed to select the primary cluster head; finally, the improved sparrow search algorithm (SSA) is applied to select the sub-cluster head within the cluster. The clustering mechanism is compared with three similar clustering protocols for experiments, and the average improvement over the other three algorithms is 121.7%, 77.1%, and 12.3%, respectively. The results show that the clustering mechanism can reduce node energy consumption and extend the network survival cycle.

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Section: Introductionmentioning

confidence: 99%

Wireless sensor networks multi-strategy clustering mechanism based on improved sparrow search algorithm

Lin¹,

Ma²,

Luo³

2023

Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023)

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“…One centroid node from those grids, which is called the initiator node, predicts whether the zone is highly active (HA), medium active (MA), and low active (LA). Here, HA zones send data continuously to the base node through the interior node, MA zones send data periodically, and LA zones do not transfer data in the status that manages power consumption effectively 18 . Another author proposed obtaining data from the sensors every 2 min if there is the potential of a forest fire or obtaining data every 15 min otherwise to reduce the energy wastage 19 .…”

Section: Introductionmentioning

confidence: 99%

Forest fire detection system using wireless sensor networks and machine learning

Dampage

Bandaranayake

Wanasinghe

et al. 2022

Sci Rep

117

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Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems.

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“…Moayedi H et al [12] used a hybrid evolutionary algorithm to build a forest fire risk prediction model; a forest fire sensitivity map of fire-prone areas in Iran was generated based on combining an adaptive neuro-fuzzy inference system, a genetic algorithm, particle swarm optimization, and differential evolution algorithm with reliable accuracy. Vikram R et al [13] used a support vector machine (SVM) for a semi-supervised classification model to divide the forest area into different subareas: high activity (HA), medium activity (MA), and low activity (LA). Due to energy limitations, a sensor node that only monitored one parameter could predict the fire risk in different areas with 90% accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network

et al. 2022

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In this work, an improved dynamic convolutional neural network (DCNN) model to accurately identify the risk of a forest fire was established based on the traditional DCNN model. First, the DCNN network model was trained in combination with transfer learning, and multiple pre-trained DCNN models were used to extract features from forest fire images. Second, principal component analysis (PCA) reconstruction technology was used in the appropriate subspace. The constructed 15-layer forest fire risk identification DCNN model named “DCN_Fire” could accurately identify core fire insurance areas. Moreover, the original and enhanced image data sets were used to evaluate the impact of data enhancement on the model’s accuracy. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. The difficulty of using DCNN to monitor forest fire risk was solved, and the model’s detection accuracy was further improved. The true positive rate was 7.41% and the false positive rate was 4.8%. When verifying the impact of different batch sizes and loss rates on verification accuracy, the loss rate of the DCN_Fire model of 0.5 and the batch size of 50 provided the optimal value for verification accuracy (0.983). The analysis results showed that the improved DCNN model had excellent recognition speed and accuracy and could accurately recognize and classify the risk of a forest fire under natural light conditions, thereby providing a technical reference for preventing and tackling forest fires.

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EEFFL: energy efficient data forwarding for forest fire detection using localization technique in wireless sensor network

Cited by 35 publications

References 34 publications

Wireless sensor networks multi-strategy clustering mechanism based on improved sparrow search algorithm

Wireless sensor networks multi-strategy clustering mechanism based on improved sparrow search algorithm

Forest fire detection system using wireless sensor networks and machine learning

A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network

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